[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100651668":3},{"organization":4,"armGroups":7,"interventions":13,"overallOfficials":10,"centralContacts":10,"locations":10,"responsibleParty":18,"collaborators":10,"id":20,"slug":21,"hasResults":22,"nctId":23,"briefTitle":24,"officialTitle":25,"acronym":10,"eligibilityCriteria":26,"healthyVolunteers":22,"sex":27,"minAge":28,"maxAge":10,"enrollmentInfo":29,"targetDuration":10,"studyType":32,"phases":10,"briefSummary":33,"conditions":34,"keywords":38,"overallStatus":42,"whyStopped":10,"lastUpdateSubmitDate":43,"lastUpdatePostDateStruct":44,"startDateStruct":47,"completionDateStruct":49,"leadSponsor":51,"locationsCount":10},{"fullName":5,"class":6},"Second Affiliated Hospital, Zhejiang University, School of Medicine","OTHER",[8],{"label":9,"type":10,"description":10,"interventionNames":11},"Critically Ill Patient Cohort",null,[12],"Other: Red Blood Cell Transfusion Exposure",[14],{"type":6,"name":15,"description":16,"armGroupLabels":17,"otherNames":10},"Red Blood Cell Transfusion Exposure","Red blood cell transfusion exposure refers to the receipt of red blood cell transfusion during intensive care hospitalization. Transfusion-related information, including transfusion status, number of transfused units, and cumulative transfusion volume, will be collected from routine clinical care records. Transfusion decisions are not assigned by the study protocol, and no intervention is performed as part of this observational study.",[9],{"type":19,"investigatorFullName":10,"investigatorTitle":10,"investigatorAffiliation":10,"oldNameTitle":10,"oldOrganization":10},"SPONSOR","100651668","ai-based-precision-transfusion-prediction-model-in-critically-ill-patients-100651668",false,"NCT07762131","AI-Based Precision Transfusion Prediction Model in Critically Ill Patients","Artificial Intelligence-Based Precision Transfusion Prediction Model for Prevention of Multiple Organ Dysfunction Syndrome in Critically Ill Patients: A Multicenter Observational Study","Inclusion Criteria:\n\n* Adult patients (aged ≥18 years) admitted to the intensive care unit.\n* Patients with available clinical data, including demographic characteristics, laboratory parameters, transfusion-related information, and clinical outcomes.\n* Patients meeting the requirements for model development and analysis.\n\nExclusion Criteria:\n\n* Patients younger than 18 years.\n* Patients with missing key clinical information required for analysis.\n* Patients with repeated ICU admissions during the study period (only the first ICU admission will be included).\n* Patients whose data cannot be used for research purposes according to ethical requirements.","ALL","18 Years",{"count":30,"type":31},2598,"ESTIMATED","OBSERVATIONAL","This multicenter observational study aims to develop and validate an artificial intelligence-based precision transfusion prediction model for critically ill patients. The study will collect clinical characteristics, laboratory parameters, transfusion-related information, physiological data, and clinical outcomes from critically ill patients admitted to intensive care units. An AI model will be developed using retrospective data and further evaluated using prospective observational data. The primary objective is to investigate factors associated with multiple organ dysfunction syndrome (MODS) and establish a predictive model to support individualized transfusion management in critically ill patients.",[35,36,37],"Critical Illness","Multiple Organ Dysfunction Syndrome","Blood Transfusion",[39,40,41,36],"Artificial Intelligence","Precision Transfusion","Critically Ill Patients","NOT_YET_RECRUITING","2026-08-11",{"date":45,"type":46},"2026-08-13","ACTUAL",{"date":48,"type":31},"2026-09-01",{"date":50,"type":31},"2029-09-01",{"name":5,"class":6}]